The field of medical imaging has seen significant advances since the time x-rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of the large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical structures and abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or medical condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or medical condition.
One general method of automatic image processing employs feature based recognition techniques to determine the presence of anatomical structures in medical images. However, feature based recognition techniques can suffer from accuracy problems.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
Cancer is one of the leading causes of death in the world. One common and often fatal type of cancer is colon or colorectal cancer. Colon cancer presents as a solid cancerous growth that is believed to begin from a benign growth (called colonic polyp) on the inner surface of the colon and rectum. Studies have shown that early identification of precursor polyps in the colonic lumen may be a critical first step in detecting, treating and preventing colon cancer.
Various methods have been proposed for screening patients for potential development of colon cancer. The use of imaging technology in concert with computer-aided diagnosis (CAD) systems has been gaining in popularity due to their minimally-invasive nature, and therefore decreased discomfort to the patient as compared to manual optical colonoscopy. CAD methods may be used to detect and highlight polyps in a three-dimensional volume, which is reconstructed from cross-sectional slices obtained by imaging technologies such as computed tomography (CT) or magnetic resonance (MR). A virtual fly-through of the reconstructed model of the colon enables physicians to examine any suspicious polyps inside the colon in a manner similar to a clinical colonoscopy. Although the time and cost of such examination can be greatly reduced by CAD methods, the task of identifying true polyps and removing false positives is complicated by the presence of different structures in the colon.
FIG. 1 shows a diagram of a cross-section of a colon 100 having different structures (102, 104, 106). As shown, the colon 100 may have a ridge-like haustral fold 102 and a flat colon wall 104. When there is a polyp 106 on a haustral fold 102, it is often difficult to detect the polyp 106 because its shape varies according to the shape of the haustral fold 102, causing the boundary between the polyp and the fold to be vague. The presence of haustral fold 102 also accounts for a large number of false positive findings because parts of the fold resemble a polyp. In order to increase the accuracy of the computer-aided detection, it is desirable to segment and distinguish folds from polyps and other structures. The detection and marking of folds also helps the user to visually correlate the various components of the colon in the prone and supine views.
There are several approaches available to assist physicians in detecting haustral folds. Unfortunately, existing techniques generally do not provide satisfactory accuracy and efficiency. For example, such techniques are unable to distinguish polyps and folds where the folds are too complex. In addition, conventional methods do not discriminate between different parts of a fold, such as the face, base or ridge. Such differentiation is particularly useful in the detection of polyps because a polyp on a face of a fold appears quite different from a polyp at the base of a fold.
As such, improved systems and methods for computer-aided fold detection that overcomes the aforementioned disadvantages are desired.